Reactivity stabilization and capacity study of fabricated alumina and zirconia‐supported <scp>CaO</scp> ‐based sorbents for high‐temperature <scp> CO <sub>2</sub> </scp> capture in a fixed‐bed reactor
Bibliographic record
Abstract
Abstract Calcium looping process is a promising approach for CO 2 capture from the flue gas of fossil fuel power plants and the cement industry. Even though the advantages of calcium‐based sorbents are low cost and high uptake capacity, they suffer from low durability during cycles. Modified sorbents were fabricated by adding alumina and zirconia and the mixture of alumina and zirconia to calcium oxide via the co‐precipitation method. The performance of synthesized sorbents in terms of stability and CO 2 capture capacity were evaluated using a fixed bed reactor in various CO 2 sorption/desorption cycles. The sorbents were fabricated by a co‐precipitation methodology using 10% binders (alumina and/or silica). X‐ray diffraction (XRD), BET/BJH, and scanning electron microscopy (SEM) were conducted for characterization of synthesized sorbents. CaO‐10% ZrO 2 showed the best performance among the fabricated sorbents in terms of stability during 5 cycles and CO 2 capacity (14 mmol CO 2 /g sorbent). The formation of CaZrO 3 with a perovskite structure and high‐temperature resistance could be attributed to well performance of zirconia‐supported sorbent. On the other hand, no sign of aluminum zirconate formation was approved in XRD analysis for the fabricated sorbent using mixed binders of zirconia and alumina to enhance its stability during cycles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".